This document summarizes a workshop on using new technologies to understand and change health behaviors. It provides two examples of how continuous digital data can provide insights: daily self-weighing data over a year showed patterns in weight variability that associated with weight management success; analysis of heart rate variability data from over 14,000 individuals revealed novel patterns in physical activity levels by weekday and month. The document concludes that data from modern wearables can help develop dynamic computational models of behavior to support health behavior change and maintenance.

1. Using new technologies for
understanding and changing
behavior
Ilkka Korhonen, Donna SprujtMetz, Niilo Saranummi, Stephen
Intille, Wendy Nielsen, Misha Pavel
Wireless Health 2013
2.11.2013

2. Our behavior is killing us…
Proportional contribution to premature death
Genetic disposition
Social circumstances
Environmental exposure
Health care
Behavior
Schroeder NEJM 2007

3. Our behavior is killing us…
Here’s the part we can change
Genetic disposition
Social circumstances
Environmental exposure
Health care
Behavior
Schroeder NEJM 2007

4. Outline
• Motivation
• Lessons from “International Workshop on New
Computationally-Enabled Theoretical Models to
Support Health Behavior Change and
Maintenance”
• Two examples of use of new technologies to
understand behaviors
– Getting under the hood of weight data
– Big data (heart rate variability)
• Conclusions
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5. International Workshop on New ComputationallyEnabled Theoretical Models to Support Health
Behavior Change and Maintenance, Brussels Oct 2012
Experts from the European Union (15) and the United States (15):
e-, tele- and mHealth (mental health, metabolic health, substance use), web
marketing, persuasive technologies, policy, physics, systems science, economics, clinical, health
& behavioral psychology, gaming, information and communications technology, artificial
intelligence, human computer interactions, health informatics, computer science, biomedical
engineering, health disparities, medicine (chronic disease, endocrinology, primary care), public
health and public health law, art and design.

6. Workshop Premise
• Current understanding of human behavior is based on static
‘snapshots’ of human behavior
Riley et al 2012, Kumar et al AJPM 2013, Spruijt-Metz et al under review

7. Our continuous digital ‘footprints’ record our
behavior in context and real time.
This allows quantification and
modeling of real behaviors in context

8. Example 1: Weight patterns
• Self-monitoring / self-weighing is
an efficient intervention for weight
loss and weight management
• Focus so far mostly in trends –
but weight varies from day to day
• Connected weight scales allow
easy and frequent long-term monitoring
• Is there information in the variability?
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9. Weight increases during weekends
and decreases during weekdays –
especially in weight losers
80 subjects
1y follow-up
instructed to
self-weigh
daily
 Weight variability pattern may associate with
weight management success
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10. Example 2: HRV & ”big data”
in collaboration with Firstbeat Technologies
www.firstbeat.fi
Number of
measurement
days
21546
Number of
individuals
14190
Age
43±10 (18-65)
BMI
26±4 (18-40)
Percentage of
males [%]
49
Activity class
4.9±1.9 (0-10)
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11. HRV analysis  classification of physiological
state, quantification physical activity
Stress
Recovery
An elevated activity level in the body caused by
external or internal factors (excluding
immediate physical demands).
A lowered activity level in the body caused by
reduction of external or internal stress factors.
Low heart rate and high HRV.
High heart rate and low HRV.

12. Physical activity* by weekday
and month in Finnish employees
Jan
34
Feb
32
Mar
30
Apr
28
May
24
Jul
22
Aug
Sep
20
Oct
18
Nov
16
Dec
14
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Minutes
26
Jun
*>3MET from 10minute
bouts, background
(age, gender, BMI,
activity class)
controlled

13. Conclusions
• Data acquired with modern wearable and
ubiquitous technologies reveals novel
patterns and relationships between
different factors which can help us to
develop dynamic computational models of
behavior.
• Future:
dynamic, personalizable, adaptable, conte
xtualized models of health behavior and
behavior change.
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14. Thank you!
Ilkka Korhonen
Personal Health Informatics/Tampere University of Technology
&
Personalized ICT for Health, VTT
ilkka.korhonen@tut.fi